Latest revision as of 22:31, 28 January 2021

Abstract

Situation awareness is required for an Unmanned Aerial Vehicle (UAV) when it makes an arrival at an uncontrolled airfield. Since no air traffic control service is available, the UAV needs to detect and track other traffic aircraft by using its onboard sensors. General aviation pilots obtain enough situation awareness to operate in these environments, only using their vision and radio messages heard from other traffic aircraft. To improve the target tracking performance of a UAV, the circuit flight rules and standard radio messages are incorporated to provide extra knowledge about the target behaviour. This is achieved by using the multiple models to describe the target motions in different flight phases and characterising the phase transition in a stochastic manner. Consequently, an interacting multiple model particle filter with state-dependent transition probabilities is developed to perform Bayesian filtering with bearing-only observations from a vision sensor.

Original document

The different versions of the original document can be found in:

• https://repository.lboro.ac.uk/articles/conference_contribution/Situation_awareness_for_UAV_operating_in_terminal_areas_using_bearing-only_observations_and_circuit_flight_rules/9223814/files/16803299.pdf under the license https://creativecommons.org/licenses/by-nc-nd

• http://xplorestaging.ieee.org/ielx7/7518121/7524873/07524960.pdf?arnumber=7524960,

http://dx.doi.org/10.1109/acc.2016.7524960 under the license cc-by-nc-nd

• https://dblp.uni-trier.de/db/conf/amcc/acc2016.html#CoombesLC16,

http://doi.org/10.1109/ACC.2016.7524960,

https://ieeexplore.ieee.org/document/7524960,

http://ieeexplore.ieee.org/document/7524960,

https://doi.org/10.1109/ACC.2016.7524960,

https://academic.microsoft.com/#/detail/2482399426